
import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_pytesseract_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor

class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			def __init__(							self			:							int    ,						snake_case_			:							Dict    ,						snake_case_			:							Any=7    ,						snake_case_			:							Tuple=3    ,						snake_case_			:							List[str]=18    ,						snake_case_			:							List[str]=30    ,						snake_case_			:							str=400    ,						snake_case_			:							Tuple=True    ,						snake_case_			:							Tuple=None    ,						snake_case_			:							Any=True    ,						):
							UpperCamelCase_:				List[str]						=    size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:				str						=    parent
							UpperCamelCase_:				Tuple						=    batch_size
							UpperCamelCase_:				Any						=    num_channels
							UpperCamelCase_:				List[str]						=    image_size
							UpperCamelCase_:				Any						=    min_resolution
							UpperCamelCase_:				Union[str, Any]						=    max_resolution
							UpperCamelCase_:				Optional[Any]						=    do_resize
							UpperCamelCase_:				Optional[int]						=    size
							UpperCamelCase_:				Optional[Any]						=    apply_ocr




			def    lowerCAmelCase__						(							self			:							int   ):
							return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}




@require_torch
@require_pytesseract
class 							_UpperCamelCase					(       _A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			LayoutLMvaImageProcessor if is_pytesseract_available() else None

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				int						=    LayoutLMvaImageProcessingTester(self   )

			@property
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							UpperCamelCase_:				Dict						=    self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(snake_case_    ,						"""do_resize"""   )   )
							self.assertTrue(hasattr(snake_case_    ,						"""size"""   )   )
							self.assertTrue(hasattr(snake_case_    ,						"""apply_ocr"""   )   )

			def    lowerCAmelCase__						(							self			:							Dict   ):
							UpperCamelCase_:				Union[str, Any]						=    self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size    ,						{"""height""": 18, """width""": 18}   )

							UpperCamelCase_:				Any						=    self.image_processing_class.from_dict(self.image_processor_dict    ,						size=42   )
							self.assertEqual(image_processor.size    ,						{"""height""": 42, """width""": 42}   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							pass

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							# Initialize image_processing
							UpperCamelCase_:				str						=    self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							UpperCamelCase_:				Any						=    prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=snake_case_   )
							for image in image_inputs:
											self.assertIsInstance(snake_case_    ,						Image.Image   )

							# Test not batched input
							UpperCamelCase_:				Dict						=    image_processing(image_inputs[0]    ,						return_tensors="""pt"""   )
							self.assertEqual(
							    encoding.pixel_values.shape    ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )    ,						)

							self.assertIsInstance(encoding.words    ,						snake_case_   )
							self.assertIsInstance(encoding.boxes    ,						snake_case_   )

							# Test batched
							UpperCamelCase_:				List[str]						=    image_processing(snake_case_    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )    ,						)

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							# Initialize image_processing
							UpperCamelCase_:				Tuple						=    self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							UpperCamelCase_:				Union[str, Any]						=    prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=snake_case_    ,						numpify=snake_case_   )
							for image in image_inputs:
											self.assertIsInstance(snake_case_    ,						np.ndarray   )

							# Test not batched input
							UpperCamelCase_:				Optional[Any]						=    image_processing(image_inputs[0]    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )    ,						)

							# Test batched
							UpperCamelCase_:				List[str]						=    image_processing(snake_case_    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )    ,						)

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							# Initialize image_processing
							UpperCamelCase_:				Any						=    self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							UpperCamelCase_:				List[Any]						=    prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=snake_case_    ,						torchify=snake_case_   )
							for image in image_inputs:
											self.assertIsInstance(snake_case_    ,						torch.Tensor   )

							# Test not batched input
							UpperCamelCase_:				List[str]						=    image_processing(image_inputs[0]    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )    ,						)

							# Test batched
							UpperCamelCase_:				List[str]						=    image_processing(snake_case_    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )    ,						)




			def    lowerCAmelCase__						(							self			:							List[str]   ):
							# with apply_OCR = True
							UpperCamelCase_:				Any						=    LayoutLMvaImageProcessor()

							from datasets import load_dataset

							UpperCamelCase_:				Union[str, Any]						=    load_dataset("""hf-internal-testing/fixtures_docvqa"""    ,						split="""test"""   )

							UpperCamelCase_:				List[str]						=    Image.open(ds[0]["""file"""]   ).convert("""RGB"""   )

							UpperCamelCase_:				List[str]						=    image_processing(snake_case_    ,						return_tensors="""pt"""   )

							self.assertEqual(encoding.pixel_values.shape    ,						(1, 3, 224, 224)   )
							self.assertEqual(len(encoding.words   )    ,						len(encoding.boxes   )   )

							# fmt: off
							# the words and boxes were obtained with Tesseract 4.1.1
							UpperCamelCase_:				Dict						=    [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
							UpperCamelCase_:				int						=    [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
							# fmt: on

							self.assertListEqual(encoding.words    ,						snake_case_   )
							self.assertListEqual(encoding.boxes    ,						snake_case_   )

							# with apply_OCR = False
							UpperCamelCase_:				Any						=    LayoutLMvaImageProcessor(apply_ocr=snake_case_   )

							UpperCamelCase_:				List[Any]						=    image_processing(snake_case_    ,						return_tensors="""pt"""   )

							self.assertEqual(encoding.pixel_values.shape    ,						(1, 3, 224, 224)   )








import collections
import os
import re
from pathlib import Path


lowerCamelCase_    :			Optional[Any]    =	"""src/transformers"""


# Matches is_xxx_available()
lowerCamelCase_    :			Union[str, Any]    =	re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCamelCase_    :			int    =	re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCamelCase_    :			Union[str, Any]    =	re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
lowerCamelCase_    :			Any    =	re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
lowerCamelCase_    :			Any    =	re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCamelCase_    :			List[Any]    =	re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCamelCase_    :			Any    =	re.compile(r"""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCamelCase_    :			Tuple    =	re.compile(r"""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
lowerCamelCase_    :			Tuple    =	re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
lowerCamelCase_    :			Dict    =	re.compile(r"""^\s*try:""")
# Catches a line with else:
lowerCamelCase_    :			Union[str, Any]    =	re.compile(r"""^\s*else:""")







def 							A__       (    lowerCamelCase    )	->						List[Any]:
				if _re_test_backend.search(lowerCamelCase    ) is None:
								return None
				UpperCamelCase_:				Any						=    [b[0] for b in _re_backend.findall(lowerCamelCase    )]
				backends.sort()
				return "_and_".join(lowerCamelCase    )







def 							A__       (    lowerCamelCase    )	->						Union[str, Any]:
				with open(lowerCamelCase  ,     """r"""  ,     encoding="""utf-8"""  ,     newline="""\n"""    ) as f:
								UpperCamelCase_:				Dict						=    f.readlines()

				UpperCamelCase_:				Tuple						=    0
				while line_index < len(lowerCamelCase    ) and not lines[line_index].startswith("""_import_structure = {"""    ):
								line_index += 1

				# If this is a traditional init, just return.
				if line_index >= len(lowerCamelCase    ):
								return None

				# First grab the objects without a specific backend in _import_structure
				UpperCamelCase_:				Optional[int]						=    []
				while not lines[line_index].startswith("""if TYPE_CHECKING"""    ) and find_backend(lines[line_index]    ) is None:
								UpperCamelCase_:				Any						=    lines[line_index]
								# If we have everything on a single line, let's deal with it.
								if _re_one_line_import_struct.search(lowerCamelCase    ):
												UpperCamelCase_:				str						=    _re_one_line_import_struct.search(lowerCamelCase    ).groups()[0]
												UpperCamelCase_:				Tuple						=    re.findall(r"""\[([^\]]+)\]"""  ,     lowerCamelCase    )
												for imp in imports:
																objects.extend([obj[1:-1] for obj in imp.split(""", """    )]    )
												line_index += 1
												continue
								UpperCamelCase_:				Any						=    _re_import_struct_key_value.search(lowerCamelCase    )
								if single_line_import_search is not None:
												UpperCamelCase_:				Any						=    [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """    ) if len(lowerCamelCase    ) > 0]
												objects.extend(lowerCamelCase    )
								elif line.startswith(""" """ * 8 + """\""""    ):
												objects.append(line[9:-3]    )
								line_index += 1

				UpperCamelCase_:				Optional[Any]						=    {"""none""": objects}
				# Let's continue with backend-specific objects in _import_structure
				while not lines[line_index].startswith("""if TYPE_CHECKING"""    ):
								# If the line is an if not is_backend_available, we grab all objects associated.
								UpperCamelCase_:				Any						=    find_backend(lines[line_index]    )
								# Check if the backend declaration is inside a try block:
								if _re_try.search(lines[line_index - 1]    ) is None:
												UpperCamelCase_:				Any						=    None

								if backend is not None:
												line_index += 1

												# Scroll until we hit the else block of try-except-else
												while _re_else.search(lines[line_index]    ) is None:
																line_index += 1

												line_index += 1

												UpperCamelCase_:				int						=    []
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(""" """ * 4    ):
																UpperCamelCase_:				Tuple						=    lines[line_index]
																if _re_import_struct_add_one.search(lowerCamelCase    ) is not None:
																				objects.append(_re_import_struct_add_one.search(lowerCamelCase    ).groups()[0]    )
																elif _re_import_struct_add_many.search(lowerCamelCase    ) is not None:
																				UpperCamelCase_:				List[str]						=    _re_import_struct_add_many.search(lowerCamelCase    ).groups()[0].split(""", """    )
																				UpperCamelCase_:				str						=    [obj[1:-1] for obj in imports if len(lowerCamelCase    ) > 0]
																				objects.extend(lowerCamelCase    )
																elif _re_between_brackets.search(lowerCamelCase    ) is not None:
																				UpperCamelCase_:				Tuple						=    _re_between_brackets.search(lowerCamelCase    ).groups()[0].split(""", """    )
																				UpperCamelCase_:				List[Any]						=    [obj[1:-1] for obj in imports if len(lowerCamelCase    ) > 0]
																				objects.extend(lowerCamelCase    )
																elif _re_quote_object.search(lowerCamelCase    ) is not None:
																				objects.append(_re_quote_object.search(lowerCamelCase    ).groups()[0]    )
																elif line.startswith(""" """ * 8 + """\""""    ):
																				objects.append(line[9:-3]    )
																elif line.startswith(""" """ * 12 + """\""""    ):
																				objects.append(line[13:-3]    )
																line_index += 1

												UpperCamelCase_:				List[str]						=    objects
								else:
												line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
				UpperCamelCase_:				List[str]						=    []
				while (
				    line_index < len(lowerCamelCase    )
				    and find_backend(lines[line_index]    ) is None
				    and not lines[line_index].startswith("""else"""    )
				):
								UpperCamelCase_:				List[str]						=    lines[line_index]
								UpperCamelCase_:				Union[str, Any]						=    _re_import.search(lowerCamelCase    )
								if single_line_import_search is not None:
												objects.extend(single_line_import_search.groups()[0].split(""", """    )    )
								elif line.startswith(""" """ * 8    ):
												objects.append(line[8:-2]    )
								line_index += 1

				UpperCamelCase_:				Any						=    {"""none""": objects}
				# Let's continue with backend-specific objects
				while line_index < len(lowerCamelCase    ):
								# If the line is an if is_backend_available, we grab all objects associated.
								UpperCamelCase_:				str						=    find_backend(lines[line_index]    )
								# Check if the backend declaration is inside a try block:
								if _re_try.search(lines[line_index - 1]    ) is None:
												UpperCamelCase_:				List[Any]						=    None

								if backend is not None:
												line_index += 1

												# Scroll until we hit the else block of try-except-else
												while _re_else.search(lines[line_index]    ) is None:
																line_index += 1

												line_index += 1

												UpperCamelCase_:				int						=    []
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(""" """ * 8    ):
																UpperCamelCase_:				Tuple						=    lines[line_index]
																UpperCamelCase_:				Union[str, Any]						=    _re_import.search(lowerCamelCase    )
																if single_line_import_search is not None:
																				objects.extend(single_line_import_search.groups()[0].split(""", """    )    )
																elif line.startswith(""" """ * 12    ):
																				objects.append(line[12:-2]    )
																line_index += 1

												UpperCamelCase_:				Optional[Any]						=    objects
								else:
												line_index += 1

				return import_dict_objects, type_hint_objects







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						List[Any]:

				def find_duplicates(lowerCamelCase    ):
								return [k for k, v in collections.Counter(lowerCamelCase    ).items() if v > 1]

				if list(import_dict_objects.keys()    ) != list(type_hint_objects.keys()    ):
								return ["Both sides of the init do not have the same backends!"]

				UpperCamelCase_:				Optional[int]						=    []
				for key in import_dict_objects.keys():
								UpperCamelCase_:				List[str]						=    find_duplicates(import_dict_objects[key]    )
								if duplicate_imports:
												errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''    )
								UpperCamelCase_:				Tuple						=    find_duplicates(type_hint_objects[key]    )
								if duplicate_type_hints:
												errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''    )

								if sorted(set(import_dict_objects[key]    )    ) != sorted(set(type_hint_objects[key]    )    ):
												UpperCamelCase_:				int						=    """base imports""" if key == """none""" else F'''{key} backend'''
												errors.append(F'''Differences for {name}:'''    )
												for a in type_hint_objects[key]:
																if a not in import_dict_objects[key]:
																				errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.'''    )
												for a in import_dict_objects[key]:
																if a not in type_hint_objects[key]:
																				errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.'''    )
				return errors







def 							A__       (    )	->						Tuple:
				UpperCamelCase_:				Optional[int]						=    []
				for root, _, files in os.walk(lowerCamelCase    ):
								if "__init__.py" in files:
												UpperCamelCase_:				Union[str, Any]						=    os.path.join(lowerCamelCase  ,     """__init__.py"""    )
												UpperCamelCase_:				Optional[int]						=    parse_init(lowerCamelCase    )
												if objects is not None:
																UpperCamelCase_:				Any						=    analyze_results(*lowerCamelCase    )
																if len(lowerCamelCase    ) > 0:
																				UpperCamelCase_:				Any						=    F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
																				failures.append("""\n""".join(lowerCamelCase    )    )
				if len(lowerCamelCase    ) > 0:
								raise ValueError("""\n\n""".join(lowerCamelCase    )    )







def 							A__       (    )	->						Any:
				UpperCamelCase_:				List[Any]						=    []
				for path, directories, files in os.walk(lowerCamelCase    ):
								for folder in directories:
												# Ignore private modules
												if folder.startswith("""_"""    ):
																directories.remove(lowerCamelCase    )
																continue
												# Ignore leftovers from branches (empty folders apart from pycache)
												if len(list((Path(lowerCamelCase    ) / folder).glob("""*.py"""    )    )    ) == 0:
																continue
												UpperCamelCase_:				str						=    str((Path(lowerCamelCase    ) / folder).relative_to(lowerCamelCase    )    )
												UpperCamelCase_:				Optional[Any]						=    short_path.replace(os.path.sep  ,     """."""    )
												submodules.append(lowerCamelCase    )
								for fname in files:
												if fname == "__init__.py":
																continue
												UpperCamelCase_:				Dict						=    str((Path(lowerCamelCase    ) / fname).relative_to(lowerCamelCase    )    )
												UpperCamelCase_:				Optional[int]						=    short_path.replace(""".py"""  ,     """"""    ).replace(os.path.sep  ,     """."""    )
												if len(submodule.split("""."""    )    ) == 1:
																submodules.append(lowerCamelCase    )
				return submodules


lowerCamelCase_    :			Optional[int]    =	[
    """convert_pytorch_checkpoint_to_tf2""",
    """modeling_flax_pytorch_utils""",
    """models.esm.openfold_utils""",
]







def 							A__       (    )	->						List[str]:
				# This is to make sure the transformers module imported is the one in the repo.
				from transformers.utils import direct_transformers_import

				UpperCamelCase_:				Optional[Any]						=    direct_transformers_import(lowerCamelCase    )

				UpperCamelCase_:				Tuple						=    set(transformers._import_structure.keys()    )
				# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
				# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
				# (potentiall re-) add them.
				with open(os.path.join(lowerCamelCase  ,     """__init__.py"""    )  ,     """r"""    ) as f:
								UpperCamelCase_:				List[Any]						=    f.read()
				import_structure_keys.update(set(re.findall(r"""import_structure\[\"([^\"]*)\"\]"""  ,     lowerCamelCase    )    )    )

				UpperCamelCase_:				Dict						=    [
				    module
				    for module in get_transformers_submodules()
				    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
				]

				if len(lowerCamelCase    ) > 0:
								UpperCamelCase_:				Dict						=    """\n""".join(F'''- {module}''' for module in module_not_registered    )
								raise ValueError(
								    """The following submodules are not properly registed in the main init of Transformers:\n"""
								    F'''{list_of_modules}\n'''
								    """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""    )


if __name__ == "__main__":
							check_all_inits()
							check_submodules()






